2012, 2(1): 45-56. doi: 10.3934/naco.2012.2.45

An AIS-based optimal control framework for longevity and task achievement of multi-robot systems

1. 

Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong Island, China, China

Received  March 2011 Revised  June 2011 Published  May 2012

Extending the longevity of autonomous agent system in real life application is a difficult task, especially in applications which require continuous high system performance. This paper presents a novel decentralized balancing controlling architecture for longevity and achievement in multi-agent robot systems based on several artificial immune systems (AIS) designs and principles. Simulation experiments have verified the proposed architecture has good capability to efficiently minimize the trade-off in system achievement while maintaining system sustainability, even in very demanding situations.
Citation: Raymond Ching Man Chan, Henry Ying Kei Lau. An AIS-based optimal control framework for longevity and task achievement of multi-robot systems. Numerical Algebra, Control & Optimization, 2012, 2 (1) : 45-56. doi: 10.3934/naco.2012.2.45
References:
[1]

AAMAS, "Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning" (eds. E. Alonso, D. Kudenko and D. Kazakov),, Springer-Verlag, (2003).

[2]

AAMAS, "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso),, Springer-Verlag, (2005).

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AAMAS, "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko),, Springer-Verlag, (2008).

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A. Bazzan, D. de Oliveira, F. Klugl and K. Nagel, To adapt or not to adapt  consequences of adapting driver and traffic light agents,, in, (2008), 1. doi: 10.1007/978-3-540-77949-0_1.

[5]

H. Brighton, S. Kirby and K. Smith, Situated cognition and the role of multi-agent models in explaining language structure,, in, (2003), 88.

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R. C. M. Chan and H. Y. K. Lau, Artificial immunity based cooperative sustainment framework for multi-agent systems,, in, (2011), 267. doi: 10.1007/978-0-85729-130-1_19.

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D. Dasgupta, An artificial immune system as a multi-agent decision support system,, in, 3814 (1998), 3816.

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D. Dasgupta, "Artificial Immune Systems and Their Applications,", Springer-Verlag, (1999). doi: 10.1007/978-3-642-59901-9.

[9]

L. N. De Castro and J. Timmis, "Artificial Immune Systems: A New Computational Intelligence Approach,", Springer, (2002).

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M. B. Dias, Z. Marc, Z. Robert and S. Anthony, Robust multirobot coordination in dynamic environments,, in, (2004), 3435.

[11]

R. Humza, O. Scholz, M. Mokhtar, J. Timmis and A. Tyrrell, Towards energy homeostasis in an autonomous self-reconfigurable modular robotic organism,, in, (2009), 21. doi: 10.1109/ComputationWorld.2009.83.

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Y. Ishida, "Immunity-Based Systems - A Design Perspective,", Springer-Verlag, (2004).

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A. Ishiguro, R. Watanabe and Y. Uchikawa, An immunological approach to dynamic behavior control for autonomous mobile robots,, in, 1 (1995), 495.

[14]

N. K. Jerne, Towards a network theory of the immune system,, Annales d'immunologie, 125C (1974), 373.

[15]

Z. Ji and D. Dasgupta, Artificial immune systems (AIS) research in the last five years,, in, (2003), 528.

[16]

M. Kefi, O. Korbaa, K. Ghedira and P. Yim, Container handling using multi-agent architecture,, in, (2007), 685.

[17]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: First KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, A. Grzech, R. J. Howlett and L. C. Jain),, Springer-Verlag, (2007).

[18]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Second KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, G. Jo, R. J. Howlett and L. C. Jain),, Springer-Verlag, (2008).

[19]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Thrid KES International Symposium (KES-AMSTA)" (eds. A. Hakansson, N. T. Nguyen, R. L. Hartung, R. J. Howlett and L. C. Jain),, Springer-Verlag, (2009).

[20]

A. Ko, H. Y. K. Lau and T. L. Lau, General suppression control framework: application in self-balancing robots,, in, (2005), 375.

[21]

H. Y. K. Lau and V. W. K. Wong, An immunity-based distributed multiagent-control framework,, IEEE Transactions on Systems, 36 (2006), 91. doi: 10.1109/TSMCA.2005.859103.

[22]

H. Y. K. Lau, V. W. K. Wong and A. K. S. Ng, A cooperative control model for multiagent-based material handling systems,, Expert Systems with Applications, 36 (2009), 233. doi: 10.1016/j.eswa.2007.09.025.

[23]

S. Lu and H. Y. K. Lau, An Immunity Inspired Real-Time Cooperative Control Framework for Networked Multi-agent Systems,, in, (2009), 234.

[24]

D. Male, J. Brostoff, D. B. Roth and I. Roitt, "Immunology,", Elsevier, (2006).

[25]

M. J. Mataric, "The Robotics Primer,", The MIT Press, (2007).

[26]

P. Matzinger, Tolerance, danger and the extended family,, Annu. Rev. Immunology, 12 (1994), 991. doi: 10.1146/annurev.immunol.12.1.991.

[27]

C. M. Ou and C. Ou, Multi-agent artificial immune systems (MAAIS) for intrusion detection: Abstraction from danger theory,, in, (2009), 11.

[28]

L. E. Parker, ALLIANCE: an architecture for fault tolerant multirobot cooperation,, IEEE Transactions on Robotics and Automation, 14 (1998), 220. doi: 10.1109/70.681242.

[29]

J. Pisokas and U. Nehmzow, Experiments in subsymbolic action planning with mobile robots,, in, (2005), 216.

[30]

, "Player Project,", 2010. Available from: , ().

[31]

W. K. Purves, D. Sadava, G. H. Orians and H. C. Heller, "Life: The Science of Biology,", 6th edition, (2001).

[32]

I. Satoh, Self-organizing Multi-agent Systems for Data Mining,, in, (2007), 165. doi: 10.1007/978-3-540-72839-9_14.

[33]

A. Servin and D. Kudenko, Multi-agent reinforcement learning for intrusion detection,, in, (2008), 211. doi: 10.1007/978-3-540-77949-0_15.

[34]

A. Smirnov, M. Pashkin, T. Levashova, N. Shilov and A. Kashevnik, Role-based decision mining for multiagent emergency response management,, in, (2007), 178. doi: 10.1007/978-3-540-72839-9_15.

[35]

L. Sompayrac, "How the Immune System Works,", Blackwell Science, (1999).

[36]

M. Strens and N. Windelinckx, Combining planning with reinforcement learning for multi-robot task allocation,, in, (2005), 260.

[37]

J. Timmis, Artificial immune systemstoday and tomorrow,, Natural Computing, 6 (2007), 1. doi: 10.1007/s11047-006-9029-1.

[38]

J. Timmis, P. Andrews, N. Owens and E. Clark, An interdisciplinary perspective on artificial immune systems,, Evolutionary Intelligence, 1 (2008), 5. doi: 10.1007/s12065-007-0004-2.

[39]

M. Wurst, Multi-agent learning by distributed feature extraction,, in, (2008), 239. doi: 10.1007/978-3-540-77949-0_17.

show all references

References:
[1]

AAMAS, "Adaptive Agents and Multi-Agent Systems: Adaptation and Multi-Agent Learning" (eds. E. Alonso, D. Kudenko and D. Kazakov),, Springer-Verlag, (2003).

[2]

AAMAS, "Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning" (eds. D. Kudenko, D. Kazakov and E. Alonso),, Springer-Verlag, (2005).

[3]

AAMAS, "Adaptive Agents and Multi-Agent Systems III: Adaptation and Multi-Agent Learning" (eds. K. Tuyls, A. Nowe, Z. Guessoum and D. Kudenko),, Springer-Verlag, (2008).

[4]

A. Bazzan, D. de Oliveira, F. Klugl and K. Nagel, To adapt or not to adapt  consequences of adapting driver and traffic light agents,, in, (2008), 1. doi: 10.1007/978-3-540-77949-0_1.

[5]

H. Brighton, S. Kirby and K. Smith, Situated cognition and the role of multi-agent models in explaining language structure,, in, (2003), 88.

[6]

R. C. M. Chan and H. Y. K. Lau, Artificial immunity based cooperative sustainment framework for multi-agent systems,, in, (2011), 267. doi: 10.1007/978-0-85729-130-1_19.

[7]

D. Dasgupta, An artificial immune system as a multi-agent decision support system,, in, 3814 (1998), 3816.

[8]

D. Dasgupta, "Artificial Immune Systems and Their Applications,", Springer-Verlag, (1999). doi: 10.1007/978-3-642-59901-9.

[9]

L. N. De Castro and J. Timmis, "Artificial Immune Systems: A New Computational Intelligence Approach,", Springer, (2002).

[10]

M. B. Dias, Z. Marc, Z. Robert and S. Anthony, Robust multirobot coordination in dynamic environments,, in, (2004), 3435.

[11]

R. Humza, O. Scholz, M. Mokhtar, J. Timmis and A. Tyrrell, Towards energy homeostasis in an autonomous self-reconfigurable modular robotic organism,, in, (2009), 21. doi: 10.1109/ComputationWorld.2009.83.

[12]

Y. Ishida, "Immunity-Based Systems - A Design Perspective,", Springer-Verlag, (2004).

[13]

A. Ishiguro, R. Watanabe and Y. Uchikawa, An immunological approach to dynamic behavior control for autonomous mobile robots,, in, 1 (1995), 495.

[14]

N. K. Jerne, Towards a network theory of the immune system,, Annales d'immunologie, 125C (1974), 373.

[15]

Z. Ji and D. Dasgupta, Artificial immune systems (AIS) research in the last five years,, in, (2003), 528.

[16]

M. Kefi, O. Korbaa, K. Ghedira and P. Yim, Container handling using multi-agent architecture,, in, (2007), 685.

[17]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: First KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, A. Grzech, R. J. Howlett and L. C. Jain),, Springer-Verlag, (2007).

[18]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Second KES International Symposium (KES-AMSTA)" (eds. N. T. Nguyen, G. Jo, R. J. Howlett and L. C. Jain),, Springer-Verlag, (2008).

[19]

KES-AMSTA, "Agent and Multi-Agent Systems: Technologies and Applications: Thrid KES International Symposium (KES-AMSTA)" (eds. A. Hakansson, N. T. Nguyen, R. L. Hartung, R. J. Howlett and L. C. Jain),, Springer-Verlag, (2009).

[20]

A. Ko, H. Y. K. Lau and T. L. Lau, General suppression control framework: application in self-balancing robots,, in, (2005), 375.

[21]

H. Y. K. Lau and V. W. K. Wong, An immunity-based distributed multiagent-control framework,, IEEE Transactions on Systems, 36 (2006), 91. doi: 10.1109/TSMCA.2005.859103.

[22]

H. Y. K. Lau, V. W. K. Wong and A. K. S. Ng, A cooperative control model for multiagent-based material handling systems,, Expert Systems with Applications, 36 (2009), 233. doi: 10.1016/j.eswa.2007.09.025.

[23]

S. Lu and H. Y. K. Lau, An Immunity Inspired Real-Time Cooperative Control Framework for Networked Multi-agent Systems,, in, (2009), 234.

[24]

D. Male, J. Brostoff, D. B. Roth and I. Roitt, "Immunology,", Elsevier, (2006).

[25]

M. J. Mataric, "The Robotics Primer,", The MIT Press, (2007).

[26]

P. Matzinger, Tolerance, danger and the extended family,, Annu. Rev. Immunology, 12 (1994), 991. doi: 10.1146/annurev.immunol.12.1.991.

[27]

C. M. Ou and C. Ou, Multi-agent artificial immune systems (MAAIS) for intrusion detection: Abstraction from danger theory,, in, (2009), 11.

[28]

L. E. Parker, ALLIANCE: an architecture for fault tolerant multirobot cooperation,, IEEE Transactions on Robotics and Automation, 14 (1998), 220. doi: 10.1109/70.681242.

[29]

J. Pisokas and U. Nehmzow, Experiments in subsymbolic action planning with mobile robots,, in, (2005), 216.

[30]

, "Player Project,", 2010. Available from: , ().

[31]

W. K. Purves, D. Sadava, G. H. Orians and H. C. Heller, "Life: The Science of Biology,", 6th edition, (2001).

[32]

I. Satoh, Self-organizing Multi-agent Systems for Data Mining,, in, (2007), 165. doi: 10.1007/978-3-540-72839-9_14.

[33]

A. Servin and D. Kudenko, Multi-agent reinforcement learning for intrusion detection,, in, (2008), 211. doi: 10.1007/978-3-540-77949-0_15.

[34]

A. Smirnov, M. Pashkin, T. Levashova, N. Shilov and A. Kashevnik, Role-based decision mining for multiagent emergency response management,, in, (2007), 178. doi: 10.1007/978-3-540-72839-9_15.

[35]

L. Sompayrac, "How the Immune System Works,", Blackwell Science, (1999).

[36]

M. Strens and N. Windelinckx, Combining planning with reinforcement learning for multi-robot task allocation,, in, (2005), 260.

[37]

J. Timmis, Artificial immune systemstoday and tomorrow,, Natural Computing, 6 (2007), 1. doi: 10.1007/s11047-006-9029-1.

[38]

J. Timmis, P. Andrews, N. Owens and E. Clark, An interdisciplinary perspective on artificial immune systems,, Evolutionary Intelligence, 1 (2008), 5. doi: 10.1007/s12065-007-0004-2.

[39]

M. Wurst, Multi-agent learning by distributed feature extraction,, in, (2008), 239. doi: 10.1007/978-3-540-77949-0_17.

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